The development of robotic systems led to their spread to various spheres of human life. Medical rehabilitation is not an exception. It actively introduces robotic devices into medical practice, replacing traditional manual therapy with procedures using high-tech robotic devices. Robotic exoskeleton for rehabilitation, despite its long history of development, has become a technical innovation that has gained wide popularity in the medical environment in the last decade. Such devices are designed to compensate lower limb disability resulting from the brain or spinal cord injuries and diseases. In this paper, a new approach that uses neuromuscular signals from lower limbs to control the exoskeleton device is proposed. A method for detecting gait phases by the signal of EMG activity has been developed. We believe that control system presented will provide a more natural and effective method to mobilize patient resources and restore motor skills after stroke.

Population spike or burst signaling is widely observed both in intact brain and neuronal cultures. Experimental evidence suggests that locally applied electrical stimuli can shape the network architecture and thus the neuronal response. However, there is no clue on how this process can be controlled. In this work we study a realistic model of a culture of cortical-like neurons with spike timing dependent plasticity (STDP). We show that the network dynamics is driven by a competition of spike-conducting pathways, which influences the learning ability of the network. Even in the case of single-electrode stimulation the network dynamics can be complex. Self-establishing spike-conducting pathways, different from those we expect to strengthen, can interfere the process of the network structuring. It leads to an intermittent regime: the time intervals of well-pronounced population spikes synchronized with the stimulus are alternated by intervals of asynchronous dynamics. Under multi-electrode stimulation of an unstructured network the competition of spike-conducting pathways destroys the unconditional learning. The STDP stimulation protocol fails to work at the network scale. To overcome this restriction we propose to use structured neural network and show that one can train such a network and achieve spiking activity circulating in the network after the stimulus has been switched off.

Cognitive object handling and manipulation are vital skills for humans and future humanoid robots. However, the fundamental bases of how our brain solves such tasks remain largely unknown. Here we provide a novel approach that describes the problem of limb movements in dynamic situations on an abstract cognitive level. The approach involves two main steps: i) Transformation of the problem from the limb workspace to the so-called handspace, which represents the limb as a point and obstacles as objects of complex shapes, and ii) Construction of a generalized cognitive map (GCM) in the handspace by a neural network simulating activation wave. The GCM enables tracing a trajectory to a target that can be followed by the limb, which ensures collision-free movement and target catching in the workspace. We validate our approach by numerical simulations on an avatar developed for a humanoid robot Poppy.

Surface electromyographic (sEMG) signals represent a superposition of the motor unit action potentials that
can be recorded by electrodes placed on the skin. Here we explore the use of an easy wearable sEMG
bracelet for a remote interaction with a computer by means of hand gestures. We propose a human-computer
interface that allows simulating “mouse” clicks by separate gestures and provides proportional
control with two degrees of freedom for flexible movement of a cursor on a computer screen. We use an
artificial neural network (ANN) for processing sEMG signals and gesture recognition both for mouse clicks
and gradual cursor movements. At the beginning the ANN goes through an optimized supervised learning
using either rigid or fuzzy class separation. In both cases the learning is fast enough and requires neither
special measurement devices nor specific knowledge from the end-user. Thus, the approach enables
building of low-budget user-friendly sEMG solutions. The interface was tested on twelve healthy subjects.
All of them were able to control the cursor and simulate mouse clicks. The collected data show that at the
beginning users may have difficulties that are reduced with the experience and the cursor movement by
hand gestures becomes smoother, similar to manipulations by a computer mouse.

Networks of spiking neurons implemented in-silico can closely mimic in-vivo neural networks and brain functions. However, their use for hybrid computations remains rather limited. In this work we report two successful cases of development of spiking neural networks for controlling mobile robots. In the first case a neural network drives a toy robot. We show that thus obtained neuroanimat is capable of synchronizing the network activity with external sensory stimuli. Then, the robot produces basic animal behaviors. In the second example we employ spiking neurons in a human-robot interface. The interface is based on a bracelet with electromyographic sensors and recognizes nine hand gestures. The recognized gestures are sent to the robot as motor commands. Our results show that all users after few trials manage to control the robot remotely. We note that in both cases besides neural networks there are no additional external algorithms employed for the decision-making.